Efficient Allocations in Double Auction Markets

This paper proposes a simple descriptive model of discrete-time double auction markets for divisible assets. As in the classical models of exchange economies, we consider a finite set of agents described by their initial endowments and preferences. Instead of the classical Walrasian-type market models, however, we assume that all trades take place in a centralized double auction where the agents communicate through sealed limit orders for buying and selling. We find that, under nonstrategic bidding, double auction clears with zero trades precisely when the agents’ current holdings are on the Pareto frontier. More interestingly, the double auctions implement Adam Smith’s “invisible hand” in the sense that, when starting from disequilibrium, repeated double auctions lead to a sequence of allocations that converges to individually rational Pareto allocations.

Optimal Liquidity-Based Trading Tactics

We consider an agent who needs to buy (or sell) a relatively small amount of assets over some fixed short time interval. We work at the highest frequency meaning that we wish to find the optimal tactic to execute our quantity using limit orders, market orders, and cancellations. To solve the agent’s control problem, we build an order book model and optimize an expected utility function based on our price impact. We derive the equations satisfied by the optimal strategy and solve them numerically. Moreover, we show that our optimal tactic enables us to outperform significantly naive execution strategies.

Trade informativeness and liquidity in Bitcoin markets

Liquid markets are driven by information asymmetries and the injection of new information in trades into market prices. Where market matching uses an electronic limit order book (LOB), limit orders traders may make suboptimal price and trade decisions based on new but incomplete information arriving with market orders. This paper measures the information asymmetries in Bitcoin trading limit order books on the Kraken platform, and compares these to prior studies on equities LOB markets. In limit order book markets, traders have the option of waiting to supply liquidity through limit orders, or immediately demanding liquidity through market orders or aggressively priced limit orders. In my multivariate analysis, I control for volatility, trading volume, trading intensity and order imbalance to isolate the effect of trade informativeness on book liquidity. The current research offers the first empirical study of Glosten (1994) to yield a positive, and credibly large transaction cost parameter. Trade and LOB datasets in this study were several orders of magnitude larger than any of the prior studies. Given the poor small sample properties of GMM, it is likely that this substantial increase in size of datasets is essential for validating the model. The research strongly supports Glosten’s seminal theoretical model of limit order book markets, showing that these are valid models of Bitcoin markets. This research empirically tested and confirmed trade informativeness as a prime driver of market liquidity in the Bitcoin market.

The Optimal Limit Prices of Limit Orders under an Extended Geometric Brownian Motion with Bankruptcy Risk

In the Black and Scholes system, the underlying asset price model follows geometric Brownian motion (GBM) with no bankruptcy risk. While GBM is a commonly used model in financial markets, bankruptcy risk should be considered in the case of a severe economic crisis, such as that caused by the COVID-19 pandemic. The omission of bankruptcy risk could considerably influence the setting of a trading strategy. In this article, we adopt an extended GBM model that considers the bankruptcy risk and study its optimal limit price problem. A limit order is a classical trading strategy for investing in stocks. First, we construct the explicit expressions of the expected discounted profit functions for sell and buy limit orders and then derive their optimal limit prices. Furthermore, via sensitivity analysis, we discuss the influence of the omission of bankruptcy risk in executing limit orders.

Analyses of Daily Market Impact Using Execution and Order Book Information

We analyzed the Tokyo Stock Exchange (TSE) for a 29-month period from August 2014 to December 2016, including every transaction and order book snapshot, and confirmed through a simple statistical test that the market impact depends on each stock. Based on a correlation analysis, we found that the market impact slowly changes over time. From an order book analysis, negative correlations were found between the market impact and the averaged limit order volumes in the order book. We also clarified that one of the factors of market impact is the volume of limit orders in the order book.

Price Discovery in the U.S. Treasury Cash Market: On Principal Trading Firms and Dealers

We explore the following question: does the trading activity of registered dealers on Treasury interdealer broker (IDB) platforms differ from that of principal trading firms (PTF), and if so, how and to what effect on market liquidity? To do so, we use a novel dataset that combines Treasury cash transaction reports from FINRA’s Trade Reporting and Compliance Engine (TRACE) and publicly available limit order book data from BrokerTec. We find that trades conducted in a limit order book setting have high permanent price impact when a PTF is the passive party, playing the role of liquidity provider. Conversely, we find that dealer trades have higher price impact when the dealer is the aggressive party, playing the role of liquidity taker. Trades in which multiple firms (whether dealers or PTFs) participate on one or both sides, however, have relatively low price impact. We interpret these results in light of theoretical models suggesting that traders with only a “small” informational advantage prefer to use (passive) limit orders, while traders with a comparatively large informational advantage prefer to use (aggressive) market orders. We also analyze the events that occurred in Treasury markets in March 2020, during the onset of the COVID-19 pandemic.

Performance and development of a thin stock market: the Stockholm Stock Exchange 1912–2017

We estimate historical stock returns for Swedish listed companies in a newly constructed data set of daily stock prices that spans more than 100 years. Stock returns exhibit all the familiar characteristics. The growth of the public sector depressed the stock market, and the process of globalization revitalized it. Banks played an important role in the early development of the stock market. There was little trading in the past, and we examine the effects on return measurement from missing data. Stock selection and the replacement of missing transaction prices through search back procedures or limit orders make little difference to a value-weighted stock price index, while ignoring the price effects of capital operations makes a big difference.

MARKET MAKING WITH ALPHA SIGNALS

We show how a market maker employs information about the momentum in the price of the asset (i.e. alpha signal) to make decisions in their liquidity provision strategy in an order-driven electronic market. The momentum in the midprice of the asset depends on the execution of liquidity taking orders and the arrival of news. Buy market orders (MOs) exert a short-lived upward pressure on the midprice, whereas sell MOs exert a short-lived downward pressure on the midprice. We employ Nasdaq high-frequency data to estimate model parameters and to illustrate the performance of the market making strategy. The market maker employs the alpha signal to minimise adverse selection costs, execute directional trades in anticipation of price changes, and to manage inventory risk. As the market maker increases their tolerance to inventory risk, the expected profits that stem from the alpha signal increase because the strategy employs more speculative MOs and performs more roundtrip trades with limit orders.